The RAPIDS cuDF library is a GPU DataFrame manipulation library based on Apache Arrow that accelerates loading, filtering, and manipulation of data for model training data preparation. The RAPIDS GPU DataFrame provides a pandas-like API that will be familiar to data scientists, so they can now build GPU-accelerated workflows more easily.
Please see the Demo Docker Repository, choosing a tag based on the NVIDIA CUDA version you’re running. This provides a ready to run Docker container with example notebooks and data, showcasing how you can utilize cuDF.
Install and update cuDF using the conda command:
conda install -c nvidia -c rapidsai -c numba -c conda-forge -c defaults cudf=0.4.0
Note: This conda installation only applies to Linux and Python versions 3.5/3.6.
Support is coming soon, please use conda for the time being.
The following instructions are for developers and contributors to cuDF OSS development. These instructions are tested on Linux Ubuntu 16.04 & 18.04. Use these instructions to build cuDF from source and contribute to its development. Other operatings systems may be compatible, but are not currently tested.
Get libcudf Dependencies
- CUDA 9.2+
- NVIDIA driver 396.44+
- Pascal architecture or better
You can obtain CUDA from https://developer.nvidia.com/cuda-downloads
cmake will download and build Apache Arrow (version 0.7.1 or 0.8+) you may need to install Boost C++ (version 1.58+) before running
# Install Boost C++ for Ubuntu 16.04/18.04 $ sudo apt-get install libboost-all-dev
# Install Boost C++ for Conda $ conda install -c conda-forge boost
Script to build cuDF from source
Build from Source
To install cuDF from source, ensure the dependencies are met and follow the steps below:
- Clone the repository and submodules
git clone --recurse-submodules https://github.com/rapidsai/cudf.git cd cudf
- Create the conda development environment
# create the conda environment (assuming in base `cudf` directory) conda env create --name cudf_dev --file conda/environments/dev_py35.yml # activate the environment source activate cudf_dev
- Build and install
libcudf. CMake depends on the
nvccexecutable being on your path or defined in
$ cd cpp # navigate to C/C++ CUDA source root directory $ mkdir build # make a build directory $ cd build # enter the build directory $ cmake .. -DCMAKE_INSTALL_PREFIX=/install/path # configure cmake ... use $CONDA_PREFIX if you're using Anaconda $ make -j # compile the libraries librmm.so, libcudf.so ... '-j' will start a parallel job using the number of physical cores available on your system $ make install # install the libraries librmm.so, libcudf.so to '/install/path'
- To run tests (Optional):
$ make test
- Build and install cffi bindings:
$ make python_cffi # build CFFI bindings for librmm.so, libcudf.so $ make install_python # build & install CFFI python bindings. Depends on cffi package from PyPi or Conda $ py.test -v # optional, run python tests on low-level python bindings
- Build the
cudfpython package, in the
- Build the
$ cd ../../python $ python setup.py build_ext --inplace
- You will also need the following environment variables, including
- To run Python tests (Optional):
$ py.test -v # run python tests on cudf python bindings
- Finally, install the Python package to your Python path:
$ python setup.py install # install cudf python bindings
Done! You are ready to develop for the cuDF OSS project.
Automated Build in Docker Container
A Dockerfile is provided with a preconfigured conda environment for building and installing cuDF from source based off of the master branch.
- Install nvidia-docker2 for Docker + GPU support
- Verify NVIDIA driver is
- Ensure CUDA 9.2+ is installed
From cudf project root run the following, to build with defaults:
$ docker build --tag cudf .
After the container is built run the container:
$ docker run --runtime=nvidia -it cudf bash
Activate the conda environment
cudf to use the newly built cuDF and libcudf libraries:
root@3f689ba9c842:/# source activate cudf (cudf) root@3f689ba9c842:/# python -c "import cudf" (cudf) root@3f689ba9c842:/#
Customizing the Build
Several build arguments are available to customize the build process of the container. These are spcified by using the Docker build-arg flag. Below is a list of the available arguments and their purpose:
|Build Argument||Default Value||Other Value(s)||Purpose|
||9.2||10.0||set CUDA version|
||ubuntu16.04||ubuntu18.04||set Ubuntu version|
||5||7||set gcc/g++ version; NOTE: gcc7 requires Ubuntu 18.04|
||This repo||Forks of cuDF||set git URL to use for
||master||Any branch name||set git branch to checkout of
||0.40.0||Not supported||set numba version|
||1.14.3||Not supported||set numpy version|
||0.20.3||Not supported||set pandas version|
||0.10.0||0.8.0+||set pyarrow version|
||3.5||3.6||set python version|
The RAPIDS suite of open source software libraries aim to enable execution of end-to-end data science and analytics pipelines entirely on GPUs. It relies on NVIDIA® CUDA® primitives for low-level compute optimization, but exposing that GPU parallelism and high-bandwidth memory speed through user-friendly Python interfaces.
Apache Arrow on GPU
The GPU version of Apache Arrow is a common API that enables efficient interchange of tabular data between processes running on the GPU. End-to-end computation on the GPU avoids unnecessary copying and converting of data off the GPU, reducing compute time and cost for high-performance analytics common in artificial intelligence workloads. As the name implies, cuDF uses the Apache Arrow columnar data format on the GPU. Currently, a subset of the features in Apache Arrow are supported.